Query generation and time difference features for supervised semantic indexing

1. A method for training a semantic indexing model comprising: providing a search engine with a first query; receiving a set of documents of a plurality of documents related to the first query from the search engine; generating, by at least one hardware processor, an expanded query by merging at least a portion of a subset of the set of the documents with the first query; and training the semantic indexing model based on the expanded query; wherein the training comprises presenting at least a portion of the plurality of documents to a user, receiving indications of which of the plurality of documents are relevant to the expanded query and which of the plurality of documents are irrelevant to the expanded query; wherein the training updates the model based on the expanded query, the documents that are relevant to the expanded query and the documents that are irrelevant to the expanded query; wherein the updating comprises modifying the model by computing the model such that ∑ ( q , d + , d - ) ⁢ max ⁡ ( 0 , 1 - f ⁡ ( q ′ , d + ) + f ⁡ ( q ′ , d - ) ) is minimized, where f is the model, q′ denotes the expanded query, d + denotes documents that are relevant to the query q′ and d − denotes documents that are irrelevant to the query q′.

2. The method of claim 1 , wherein the method further comprises: re-ranking the set of documents based on the expanded query using the semantic indexing model.

3. The method of claim 1 , wherein the receiving further comprises selecting the subset by applying a cosine distance between a vector denoting the first query and vectors denoting the documents in the set.

4. The method of claim 1 , wherein the merging comprises merging words of said subset with words of the first query that have a particular similarity to the words of the subset.

5. The method of claim 4 , wherein the particular similarity is a co-occurrence based measure.

6. A system for training a semantic indexing model comprising: search engine, which is configured to receive a first query and generate a set of documents of a plurality of documents related to the first query; a query generator unit, implemented by at least one hardware processor, configured to generate an expanded query by merging at least a portion of a subset of the set of documents with the first query; and a controller configured to train the semantic indexing model based on the expanded query; wherein the training by the controller comprises presenting at least a portion of the plurality of documents to a user, receiving indications of which of the plurality of documents are relevant to the expanded query and which of the plurality of documents are irrelevant to the expanded query; wherein the training updates the model based on the expanded query, the documents that are relevant to the expanded query and the documents that are irrelevant to the expanded query; wherein the updating comprises modifying the model by computing the model such that ∑ ( q , d + , d - ) ⁢ max ⁡ ( 0 , 1 - f ⁡ ( q ′ , d + ) + f ⁡ ( q ′ , d - ) ) is minimized, where f is the model, q′ denotes the expanded query, d + denotes documents that are relevant to the query q′ and d − denotes documents that are irrelevant to the query q′.

7. The system of claim 6 , wherein the merging comprises merging words of said subset with words of the first query that have a particular similarity to the words of the subset.

8. The system of claim 6 , further comprising: a ranker configured to re-rank the set of documents based on the expanded query using the semantic indexing model.

9. The system of claim 6 , further comprising: a time difference module configured to determine at least one time difference parameter denoting a time difference between a receipt of the query and a generation of at least one document of the plurality of documents and to modify a similarity measure of the model based on the at least one time difference parameter.